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In this work, we propose to progressively increase the training difficultyduring learning a neural network model via a novel strategy which we callmini-batch trimming. This strategy makes sure that the optimizer puts its focusin the later training stages on the more difficult samples, which we identifyas the ones with the highest loss in the current mini-batch. The strategy isvery easy to integrate into an existing training pipeline and does notnecessitate a change of the network model. Experiments on several imageclassification problems show that mini-batch trimming is able to increase thegeneralization ability (measured via final test error) of the trained model.

Some like it tough: Improving model generalization via progressively increasing the training difficulty